ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Analysis of COVID-19 case numbers: adjustment for diagnostic misclassification on the example of German case reporting data
This article has 7 authors:Reviewed by ScreenIT
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High Infection Secondary Attack Rates of Severe Acute Respiratory Syndrome Coronavirus 2 in Dutch Households Revealed by Dense Sampling
This article has 9 authors:Reviewed by ScreenIT
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Plasma Markers of Disrupted Gut Permeability in Severe COVID-19 Patients
This article has 18 authors:Reviewed by ScreenIT
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Columbia Open Health Data for COVID-19 Research: Database Analysis
This article has 7 authors:Reviewed by ScreenIT
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Women in healthcare experiencing occupational stress and burnout during COVID-19: a rapid review
This article has 4 authors:Reviewed by ScreenIT
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Transmission of Respiratory Infectious Diseases between Neighboring Cities using Agent-based Model and Compartmental Model
This article has 5 authors:Reviewed by ScreenIT
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Reducing travel-related SARS-CoV-2 transmission with layered mitigation measures: symptom monitoring, quarantine, and testing
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 national lockdown in morocco: Impacts on air quality and public health
This article has 5 authors:Reviewed by ScreenIT
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Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion
This article has 7 authors:Reviewed by ScreenIT
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Mortality from COVID in Colombia and Peru: Analyses of Mortality Data and Statistical Forecasts
This article has 9 authors:Reviewed by ScreenIT